The present invention relates generally to improved method and apparatus for looking at energy use strategically. More specifically, the present invention relates to dynamic facility models that highlight opportunities to reduce resource consumption while allowing a facility manager to cost-effectively meet facility resource requirements, by facility modelization for facility benchmarking.
Facility managers often face a daunting task in their efforts to reduce costs associated with the consumption of electricity, gas and water (i.e., resources generally). Consider as an example the infrastructure of a typical major airport. The facility manager must cost-effectively provide the utility needs for an infrastructure consisting of main runways, aircraft taxiing and parking areas, main airport buildings, passenger transport systems, and buildings housing franchised companies spread over several miles of land. Similarly, the facility managers for public school systems, with monumental infrastructure needs, struggle to deliver the best educational environment for the fewest dollars. In certain more heavily competitive private markets, facility managers for giant retail stores, such as Wal-Mart, must cost-effectively supply the resource needs for stores located in diverse areas of the country.
Cost-effectively meeting the resource demands of diverse facilities is a complicated task. Different facilities, while having similar attributes, will often have different resource utilization patterns. In fact, the resource utilization patterns for a particular facility may vary over time due to changes in the facility attributes. Consequently, to accurately model a facility and to then accurately benchmark facility resource consumption, a dynamic modeling process is required (i.e., a process that not only considers facility attributes, but changes in facility attributes over time).
Conventional practices, while using the well-known concept of data normalization, does not typically have the ability to track changes in facility attributes over time. When a process does not have the ability to track changes in facility attributes, any such facility model will tend to be or to become inaccurate and any data generated using such a model would likewise be inaccurate.
For example, if one wishes to know the amount of electrical power a facility uses per employee, for the period of one year, one must determine the total power used and the number of people employed, at all times during the year in question in order to generate accurate data. Using conventional methodology, one could obtain the power utilization data from the power utility. However, unless the number of employees remained constant during the year in question, one would be forced to estimate the number of employees employed during the subject year. Finally, to obtain normalized data, such a conventional process would divide the power utilization data by the estimated number of employees. While such information might be useful to a limited extent, as noted, it would not be completely accurate.
Likewise, changing the number of personal computers, copiers, vending machines, employees or the number of offices (among many other potential changes) all can significantly affect the electrical load on a facility. Therefore, such conventional modeling methodology would generate inaccurate data if the attributes of interest were the number of computers, number of offices, or any other attribute that changed during the time period of interest.